New NIH Grant to Support Research on Deep Learning for CT Image Standardization

Lung cancer is the leading cause of cancer death and one of the most common cancers among both men and women in the United States. Recent advances in high-resolution imaging set the stage for radiomics to become an active emerging field in cancer research. However, the promise of radiomics is limited by a lack of image standardization tools, because computed tomography (CT) images are often acquired using scanners from different vendors with customized acquisition parameters, posing a fundamental challenge to radiomic studies across sites. To overcome this challenge, especially for large-scale, multi-site radiomic studies, advanced algorithms are required to integrate, standardize, and normalize CT images from multiple sources. We propose to develop STAN-CT, a deep learning software package that can automatically standardize and normalize a large volume of diagnostic images to facilitate cross-site large-scale image feature extraction for lung cancer characterization and stratification. STAN-CT will enable a wide range of radiomic researches to identify diagnostic image features that strongly associated with lung cancer prognosis.